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The Finite Element Method Defined

The Finite Element Method Defined. The Finite Element Method (FEM) is a weighted residual method that uses compactly-supported basis functions. Brief Comparison with Other Methods. Finite Difference (FD) Method:

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The Finite Element Method Defined

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  1. The Finite Element Method Defined The Finite Element Method (FEM) is a weighted residual method that uses compactly-supported basis functions.

  2. Brief Comparison with Other Methods Finite Difference (FD) Method: FD approximates an operator (e.g., the derivative) and solves a problem on a set of points (the grid) Finite Element (FE) Method: FE uses exact operators but approximates the solution basis functions. Also, FE solves a problem on the interiors of grid cells (and optionally on the gridpoints as well).

  3. Brief Comparison with Other Methods Spectral Methods: Spectral methods use global basis functions to approximate a solution across the entire domain. Finite Element (FE) Method: FE methods use compact basis functions to approximate a solution on individual elements.

  4. Overview of the Finite Element Method Strong form Weak form Galerkin approx. Matrix form

  5. Sample Problem Axial deformation of a bar subjected to a uniform load (1-D Poisson equation) L u = axial displacement E=Young’s modulus = 1 A=Cross-sectional area = 1

  6. Strong Form The set of governing PDE’s, with boundary conditions, is called the “strong form” of the problem. Hence, our strong form is (Poisson equation in 1-D):

  7. Weak Form We now reformulate the problem into the weak form. The weak form is a variational statement of the problem in which we integrate against a test function. The choice of test function is up to us. This has the effect of relaxing the problem; instead of finding an exact solution everywhere, we are finding a solution that satisfies the strong form on average over the domain.

  8. Weak Form Strong Form Residual R=0 Weak Form v is our test function We will choose the test function later.

  9. Weak Form Why is it “weak”? It is a weaker statement of the problem. A solution of the strong form will also satisfy the weak form, but not vice versa. Analogous to “weak” and “strong” convergence:

  10. Weak Form Choosing the test function: We can choose any v we want, so let's choose v such that it satisfies homogeneous boundary conditions wherever the actual solution satisfies Dirichlet boundary conditions. We’ll see why this helps us, and later will do it with more mathematical rigor. So in our example, u(0)=0so let v(0)=0.

  11. Weak Form Returning to the weak form: Integrate LHS by parts: Integrate

  12. Weak Form Recall the boundary conditions on u and v: Hence, H The weak form satisfies Neumann conditions automatically!

  13. Weak Form Why is it “variational”? u and v are functions from an infinite-dimensional function space H

  14. Galerkin’s Method We still haven’t done the “finite element method” yet, we have just restated the problem in the weak formulation. So what makes it “finite elements”? Solving the problem locally on elements Finite-dimensional approximation to an infinite- dimensional space → Galerkin’s Method

  15. Galerkin’s Method

  16. Galerkin’s Method

  17. Galerkin’s Method

  18. Galerkin’s Method So what have we done so far? 1) Reformulated the problem in the weak form. 2) Chosen a finite-dimensional approximation to the solution. Recall weak form written in terms of residual: This is an L2 inner-product. Therefore, the residual is orthogonal to our space of basis functions. “Orthogonality Condition”

  19. Orthogonality Condition The residual is orthogonal to our space of basis functions: u H Hh uh φi Therefore, given some space of approximate functions Hh, we are finding uh that is closest (as measured by the L2 inner product) to the actual solution u.

  20. Discretization and Basis Functions Let’s continue with our sample problem. Now we discretize our domain. For this example, we will discretize x=[0, L] into 2 “elements”. 0 h 2h=L In 1-D, elements are segments. In 2-D, they are triangles, tetrads, etc. In 3-D, they are solids, such as tetrahedra. We will solve the Galerkin problem on each element.

  21. Discretization and Basis Functions For a set of basis functions, we can choose anything. For simplicity here, we choose piecewise linear “hat functions”. Our solution will be a linear combination of these functions. φ3 φ1 φ2 x1=0 x2=L/2 x3=L

  22. Discretization and Basis Functions To save time, we can throw out φ1 a priori because, since in this example u(0)=0, we know that the coefficent c1 must be 0. φ3 φ2 x1=0 x2=L/2 x3=L

  23. Basis Functions φ3 φ2 x1=0 x2=L/2 x3=L

  24. Matrix Formulation

  25. Solution

  26. Solution Notice the numerical solution is “interpolatory”, or nodally exact.

  27. Concluding Remarks • Because basis functions are compact, matrix K is typically tridiagonal or otherwise sparse, which allows for fast solvers that take advantage of the structure (regular Gaussian elimination is O(N3), where N is number of elements!). Memory requirements are also reduced. • Continuity between elements not required. “Discontinuous Galerkin” Method

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